System and method for optimal sensor placement

ABSTRACT

A controller includes a memory that stores instructions and a processor that executes the instructions. The instructions cause the controller to execute a process that includes receiving sensor data from a first sensor and a second sensor. The sensor data includes a time-series observation representing a first activity and a second activity. The controller generates models for each activity involving progressions through states indicated by the sensor data from each sensor. The controller receives from each sensor additional sensor data including a time-series observation representing the first activity and the second activity. The controller determines likelihoods that the models generated a portion of the additional sensor data and calculates a pair-wise distance between each sensor-specific determined likelihood to obtain calculated distances. The calculated distances for each sensor are grouped, and a relevance of each sensor to each activity is determined by executing a regression model using the grouped calculated distances.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/750,375 filed on Oct. 25, 2018 and U.S. ProvisionalApplication No. 62/625,932 filed on Feb. 2, 2018. These applications arehereby incorporated by reference herein.

BACKGROUND

Sensing technologies may require arrangements of sensors for ambientsensing and/or physiological sensing. The number and placement ofsensors in the arrangements may vary based on context. For example,ambient sensing of activity of daily living (ADL) for variousindividuals, including, but not limited to, at-risk individuals is anemerging field of technology. ADLs include getting around a home toprepare a meal, eat, and use the toilet. By way of example, at-riskindividuals may include individuals with cognitive impairment and/orphysical disability.

In ambient sensing scenarios, the number, location, and type of sensorsin an arrangement are commonly selected in an ad-hoc manner. Thisresults in inefficiencies such as unnecessary costs and irrelevant datastreams. This ad hoc placement also presents many technical challengesrelated to, for example, the data that is captured and/or the processingor determinations associated with the captured data. For example, ad hocplacement may result in obscuring data streams most salient to theactivities/events of interests, impeding predictive and discriminativeanalysis of the ADLs and/or increasing computational and maintenanceexpenses.

Demographics are contributing to the increased use of ambient sensingtechnologies. For instance, the U.S. population 65 or older grew by 28%from 2004 to 2014 to approximately 46.2 million people and is projectedto grow to 82.3 million by the year of 2040. The U.S. population 85 orolder is projected to triple from approximately 6.2 million in 2014 toapproximately 14.6 million in 2040. In the United States, 93% ofMedicare beneficiaries 65 and older are aging in place, so as to stay intheir own homes and communities as they age. In Canada, 92% ofindividuals 65 and older are aging in place. According to the AmericanAssociation of Retired Persons (AARP), 90% of seniors 65 and olderprefer to age in place. The demographic shift to an older populationmeans that the younger caregiving segment of the population isshrinking. This and the burgeoning older population necessitatesdevelopment of technologies that enable independent and healthy aging inplace.

Determining an arrangement of sensor to be deployed in an ambientsetting such as a user's home is often a challenging task, especiallydue to difference in a) floor map of users' home, b) users' physicalbuild and lifestyle, and c) experience level of a technician that setsup the sensors. Additionally, as sensing technologies advance, sensoroutputs are often multi-modal and multi-variate time-seriesobservations, some of which are unnecessary and contribute to theinefficient irrelevant and or redundant data streams noted above. Theirrelevant and/or redundant modalities and variables confounddiscriminative analysis and obscure the true mapping between theobserved data streams and corresponding activities. For instance, in anambient health monitoring setup, it is often unclear what types, numbersand locations of sensors will enable seamless observation of ADLs.

Conventional approaches do not account for temporal dependencies betweensequentially-observed sensor data and may require a fixed-lengthrepresentation of the sequential observations. For example, time-seriessignals are often represented in terms of a set of pre-defined discretesingle-valued meta-features such as maximum, minimum, or variance ofsignals. However, streams of sensor data collected in a naturalisticsetting often reflect temporal dependencies and are not inherentlyconstrained to fixed-length representation. That is, in a naturalisticsetting, activities may vary in terms of time, phase, action sequenceorder, and behavior, and may result in variations in sensor dataassociated with the same activity, thus, rendering the activityrecognition difficult.

Selection of proper set of meta-features is critical for subsequentexploratory and predictive analysis. However, these meta-features areoften selected in an ad-hoc manner without regard to saliency oftemporal progression of an action sequence. Furthermore, time-seriesobservations need to be spatiotemporally-aligned in order to obtain ahomogeneous set of meta-features across different observations. Moreimportantly, these meta-features do not capture the dynamiccharacteristics and temporal progression salient to the events ofinterest.

Therefore, an approach is needed to identify salient time-seriesvariables that explicitly encodes temporal progression of sensor dataand enable handling variable-length time-series observations. In ambientsensing, such an approach will help reduce numbers of sensors to onlythe salient ones, which in turn results in a more efficient, accurate,and inexpensive solution. Accordingly, systems and methods to determinean optimal number of placement of sensing technologies is needed.

SUMMARY

According to an aspect of the present disclosure, a controller fordetermining an arrangement of sensors includes a memory that storesinstructions and a processor that executes the instructions. Whenexecuted by the processor, the instructions cause the controller toexecute a process that includes receiving, from a first sensor of atleast two sensors, a first sensor data including at least onetime-series observation representing at least a first activity and asecond activity; and receiving, from a second sensor of the at least twosensors, a second sensor data including at least one time-seriesobservation representing the first activity and the second activity. Theprocess also includes generating, by the processor, a first model forthe first activity involving a first progression through multiple statesindicated by at least a portion of the first sensor data; generating, bythe processor, a second model for the second activity involving a secondprogression through multiple states indicated by at least a portion ofthe first sensor data; generating, by the processor, a third model forthe first activity involving a third progression through multiple statesindicated by at least a portion of the second sensor data; andgenerating, by the processor, a fourth model for the second activityinvolving a fourth progression through multiple states indicated by atleast a portion of the second sensor data. The process further includesreceiving, from the first sensor, a third sensor data including at leastone time-series observation representing at least the first activity andthe second activity; and receiving, from the second sensor, a fourthsensor data including at least one time-series observation representingat least the first activity and the second activity. The processmoreover includes determining, using the processor, a likelihood thatthe first model generated at least a portion of the third sensor data, alikelihood that the second model generated at least a portion of thethird sensor data, a likelihood that the third model generated at leasta portion of the fourth sensor data, and a likelihood that the fourthmodel generated at least a portion of the fourth sensor data. Theprocessor also calculates a pair-wise distance between eachsensor-specific determined likelihood to obtain calculated distances,groups the calculated distances for the likelihoods involving the firstsensor, and groups the calculated distances for the likelihoodsinvolving the second sensor, to obtain grouped calculated distances.Finally, the process includes determining a first relevance of the firstsensor and a second relevance of the second sensor for capturing thefirst activity and the second activity by executing a regression modelusing the grouped calculated distances.

According to another aspect of the present disclosure, a method fordetermining an arrangement of sensors includes receiving, from a firstsensor of at least two sensors, a first sensor data including at leastone time-series observation representing at least a first activity and asecond activity; and receiving, from a second sensor of the at least twosensors, a second sensor data including at least one time-seriesobservation representing the first activity and the second activity;.The method also includes generating a first model for the first activityinvolving a first progression through multiple states indicated by atleast a portion of the first sensor data; generating a second model forthe second activity involving a second progression through multiplestates indicated by at least a portion of the first sensor data;generating a third model for the first activity involving a thirdprogression through multiple states indicated by at least a portion ofthe second sensor data; and generating a fourth model for the secondactivity involving a fourth progression through multiple statesindicated by at least a portion of the second sensor data. The methodfurther includes receiving, from the first sensor, a third sensor dataincluding at least one time-series observation representing at least thefirst activity and the second activity; and receiving, from the secondsensor, a fourth sensor data including at least one time-seriesobservation representing at least the first activity and the secondactivity. The method moreover includes determining a likelihood that thefirst model generated at least a portion of the third sensor data, alikelihood that the second model generated at least a portion of thethird sensor data, a likelihood that the third model generated at leasta portion of the fourth sensor data, and a likelihood that the fourthmodel generated at least a portion of the fourth sensor data. The methodalso includes calculating a pair-wise distance between eachsensor-specific determined likelihood to obtain calculated distances,grouping the calculated distances for the likelihoods involving thefirst sensor, and grouping the calculated distances for the likelihoodsinvolving the second sensor, to obtain grouped calculated distances.Finally, the method includes determining a first relevance of the firstsensor and a second relevance of the second sensor for capturing thefirst activity and the second activity by executing a regression modelusing the grouped calculated distances.

According to yet another aspect of the present disclosure, a system fordetermining an arrangement of sensors includes a communicationsinterface used to communicate over a communications network; a userinterface; and a controller including a memory that stores instructionsand a processor that executes the instructions. When executed by theprocessor, the instructions cause the system to execute a process thatincludes receiving, from a first sensor of at least two sensors, a firstsensor data including at least one time-series observation representingat least a first activity and a second activity; and receiving, from asecond sensor of the at least two sensors, a second sensor dataincluding at least one time-series observation representing the firstactivity and the second activity. The process also includes generating,by the processor, a first model for the first activity involving a firstprogression through multiple states indicated by at least a portion ofthe first sensor data; generating, by the processor, a second model forthe second activity involving a second progression through multiplestates indicated by at least a portion of the first sensor data;generating, by the processor, a third model for the first activityinvolving a third progression through multiple states indicated by atleast a portion of the second sensor data; and generating, by theprocessor, a fourth model for the second activity involving a fourthprogression through multiple states indicated by at least a portion ofthe second sensor data. The process further includes receiving, from thefirst sensor, a third sensor data including at least one time-seriesobservation representing at least the first activity and the secondactivity; and receiving, from the second sensor, a fourth sensor dataincluding at least one time-series observation representing at least thefirst activity and the second activity. The process moreover includesdetermining, using the processor, a likelihood that the first modelgenerated at least a portion of the third sensor data, a likelihood thatthe second model generated at least a portion of the third sensor data,a likelihood that the third model generated at least a portion of thefourth sensor data, and a likelihood that the fourth model generated atleast a portion of the fourth sensor data. The processor also calculatesa pair-wise distance between each sensor-specific determined likelihoodto obtain calculated distances, groups the calculated distances for thelikelihoods involving the first sensor, and groups the calculateddistances for the likelihoods involving the second sensor, to obtaingrouped calculated distances. Finally, the process includes determininga first relevance of the first sensor and a second relevance of thesecond sensor for capturing the first activity and the second activityby executing a regression model using the grouped calculated distances.

BRIEF DESCRIPTION OF THE DRAWINGS

The example embodiments are best understood from the following detaileddescription when read with the accompanying drawing figures. It isemphasized that the various features are not necessarily drawn to scale.In fact, the dimensions may be arbitrarily increased or decreased forclarity of discussion. Wherever applicable and practical, like referencenumerals refer to like elements.

FIG. 1 illustrates a general computer system, on which a method ofdetermining an arrangement of sensors can be implemented, in accordancewith a representative embodiment.

FIG. 2A is an illustrative view of a building layout for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 2B is an illustrative view of a model for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 2C is an illustrative view of a physiological layout fordetermining an arrangement of sensors, in accordance with arepresentative embodiment.

FIG. 3A is an illustrative view of a controller for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 3B is an illustrative view of a system for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 4 illustrates a process for determining an arrangement of sensors,in accordance with a representative embodiment.

FIG. 5 illustrates another process for determining an arrangement ofsensors, in accordance with a representative embodiment.

FIG. 6 illustrates another process for determining an arrangement ofsensors, in accordance with a representative embodiment.

FIG. 7 is an illustrative view of another system for determining anarrangement of sensors, in accordance with a representative embodiment.

DETAILED DESCRIPTION

In the following detailed description, for purposes of explanation andnot limitation, representative embodiments disclosing specific detailsare set forth in order to provide a thorough understanding of anembodiment according to the present teachings. Descriptions of knownsystems, devices, materials, methods of operation and methods ofmanufacture may be omitted so as to avoid obscuring the description ofthe representative embodiments. Nonetheless, systems, devices, materialsand methods that are within the purview of one of ordinary skill in theart are within the scope of the present teachings and may be used inaccordance with the representative embodiments. It is to be understoodthat the terminology used herein is for purposes of describingparticular embodiments only and is not intended to be limiting. Thedefined terms are in addition to the technical and scientific meaningsof the defined terms as commonly understood and accepted in thetechnical field of the present teachings.

It will be understood that, although the terms first, second, third etc.may be used herein to describe various elements or components, theseelements or components should not be limited by these terms. These termsare only used to distinguish one element or component from anotherelement or component. Thus, a first element or component discussed belowcould be termed a second element or component without departing from theteachings of the inventive concept.

The terminology used herein is for purposes of describing particularembodiments only and is not intended to be limiting. As used in thespecification and appended claims, the singular forms of terms ‘a’, ‘an’and ‘the’ are intended to include both singular and plural forms, unlessthe context clearly dictates otherwise. Additionally, the terms“comprises”, and/or “comprising,” and/or similar terms when used in thisspecification, specify the presence of stated features, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, elements, components, and/or groups thereof. As usedherein, the term “and/or” includes any and all combinations of one ormore of the associated listed items.

Unless otherwise noted, when an element or component is said to be“connected to”, “coupled to”, or “adjacent to” another element orcomponent, it will be understood that the element or component can bedirectly connected or coupled to the other element or component, orintervening elements or components may be present. That is, these andsimilar terms encompass cases where one or more intermediate elements orcomponents may be employed to connect two elements or components.However, when an element or component is said to be “directly connected”to another element or component, this encompasses only cases where thetwo elements or components are connected to each other without anyintermediate or intervening elements or components.

In view of the foregoing, the present disclosure, through one or more ofits various aspects, embodiments and/or specific features orsub-components, is thus intended to bring out one or more of theadvantages as specifically noted below. For purposes of explanation andnot limitation, example embodiments disclosing specific details are setforth in order to provide a thorough understanding of an embodimentaccording to the present teachings. However, other embodimentsconsistent with the present disclosure that depart from specific detailsdisclosed herein remain within the scope of the appended claims.Moreover, descriptions of well-known apparatuses and methods may beomitted so as to not obscure the description of the example embodiments.Such methods and apparatuses are within the scope of the presentdisclosure.

Various embodiments of the present disclosure provide systems, methods,and apparatus for determining an optimal configuration of sensorplacement. Advantageously and in an exemplary embodiment, a system fordetermining an optimal sensor configuration may include determining anoptimal ambient sensor confirmation, determining an optimalphysiological sensor configuration, and/or determining an optimalconfiguration of a combination of ambient and/or physiological sensingtechnologies. Although described separately, the methods of determiningambient sensing technology configurations may be used in conjunctionwith the methods of determining physiological sensing technologyconfigurations.

Regarding ambient sensing technologies, systems and methods describedherein may automatically identify a minimal set of ambient sensors andtheir optimal employment to monitor and best track activities of dailyliving (ADL). The systems and methods described herein may incorporate anumber of data points to determine optimal sensor configurations, suchas raw sensor data obtained during a training period, floorplan orlayout data associated with the sensing environment, a user's weight,height, and/or build information, a user's medical condition, aninstallment technician's skill level, and/or other data that may berelevant.

FIG. 1 illustrates a general computer system, on which a method ofdetermining an arrangement of sensors can be implemented, in accordancewith a representative embodiment.

FIG. 1 is an illustrative embodiment of a general computer system, onwhich a method of optimal sensor placement can be implemented. Thecomputer system 100 can include a set of instructions that can beexecuted to cause the computer system 100 to perform any one or more ofthe methods or computer-based functions disclosed herein. The computersystem 100 may operate as a standalone device or may be connected, forexample, using a network 101, to other computer systems or peripheraldevices.

In a networked deployment, the computer system 100 may operate in thecapacity of a server or as a client user computer in a server-clientuser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 100 can alsobe implemented as or incorporated into various devices, such as astationary computer, a mobile computer, a personal computer (PC), alaptop computer, a tablet computer, a wireless smart phone, a personaldigital assistant (PDA), or any other machine capable of executing a setof instructions (sequential or otherwise) that specify actions to betaken by that machine. The computer system 100 can be incorporated as orin a device that in turn is in an integrated system that includesadditional devices. In an embodiment, the computer system 100 can beimplemented using electronic devices that provide voice, video or datacommunication. Further, while the computer system 100 is illustrated inthe singular, the term “system” shall also be taken to include anycollection of systems or sub-systems that individually or jointlyexecute a set, or multiple sets, of instructions to perform one or morecomputer functions.

As illustrated in FIG. 1, the computer system 100 includes a processor110. A processor for a computer system 100 is tangible andnon-transitory. As used herein, the term “non-transitory” is to beinterpreted not as an eternal characteristic of a state, but as acharacteristic of a state that will last for a period. The term“non-transitory” specifically disavows fleeting characteristics such ascharacteristics of a carrier wave or signal or other forms that existonly transitorily in any place at any time. A processor is an article ofmanufacture and/or a machine component. A processor for a computersystem 100 is configured to execute software instructions to performfunctions as described in the various embodiments herein. A processorfor a computer system 100 may be a general-purpose processor or may bepart of an application specific integrated circuit (ASIC). A processorfor a computer system 100 may also be a microprocessor, a microcomputer,a processor chip, a controller, a microcontroller, a digital signalprocessor (DSP), a state machine, or a programmable logic device. Aprocessor for a computer system 100 may also be a logical circuit,including a programmable gate array (PGA) such as a field programmablegate array (FPGA), or another type of circuit that includes discretegate and/or transistor logic. A processor for a computer system 100 maybe a central processing unit (CPU), a graphics processing unit (GPU), orboth. Additionally, any processor described herein may include multipleprocessors, parallel processors, or both. Multiple processors may beincluded in, or coupled to, a single device or multiple devices.

Moreover, the computer system 100 may include a main memory 120 and/or astatic memory 130, where the memories may communicate with each othervia a bus 108. Memories described herein are tangible storage mediumsthat can store data and executable instructions and are non-transitoryduring the time instructions are stored therein. As used herein, theterm “non-transitory” is to be interpreted not as an eternalcharacteristic of a state, but as a characteristic of a state that willlast for a period. The term “non-transitory” specifically disavowsfleeting characteristics such as characteristics of a carrier wave orsignal or other forms that exist only transitorily in any place at anytime. A memory described herein is an article of manufacture and/ormachine component. Memories described herein are computer-readablemediums from which data and executable instructions can be read by acomputer. Memories as described herein may be random access memory(RAM), read only memory (ROM), flash memory, electrically programmableread only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), registers, a hard disk, a removable disk, tape, compactdisk read only memory (CD-ROM), digital versatile disk (DVD), floppydisk, blu-ray disk, or any other form of storage medium known in theart. Memories may be volatile or non-volatile, secure and/or encrypted,unsecure and/or unencrypted.

As shown, the computer system 100 may further include a video displayunit 150, such as a liquid crystal display (LCD), an organic lightemitting diode (OLED), a flat panel display, a solid-state display, or acathode ray tube (CRT). Additionally, the computer system 100 mayinclude an input device 160, such as a keyboard/virtual keyboard ortouch-sensitive input screen or speech input with speech recognition,and a cursor control device 170, such as a mouse or touch-sensitiveinput screen or pad. The computer system 100 can also include a diskdrive unit 180, a signal generation device 190, such as a speaker orremote control, and a network interface device 140.

In an embodiment, as depicted in FIG. 1, the disk drive unit 180 mayinclude a computer-readable medium 182 in which one or more sets ofinstructions 184, e.g. software, can be embedded. Sets of instructions184 can be read from the computer-readable medium 182. Further, theinstructions 184, when executed by a processor, can be used to performone or more of the methods and processes as described herein. In anembodiment, the instructions 184 may reside completely, or at leastpartially, within the main memory 120, the static memory 130, and/orwithin the processor 110 during execution by the computer system 100.

In an alternative embodiment, dedicated hardware implementations, suchas application-specific integrated circuits (ASICs), programmable logicarrays and other hardware components, can be constructed to implementone or more of the methods described herein. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules.Accordingly, the present disclosure encompasses software, firmware, andhardware implementations. Nothing in the present application should beinterpreted as being implemented or implementable solely with softwareand not hardware such as a tangible non-transitory processor and/ormemory.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented using a hardware computersystem that executes software programs. Further, in an exemplary,non-limited embodiment, implementations can include distributedprocessing, component/object distributed processing, and parallelprocessing. Virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein, and a processor described herein may be used to support avirtual processing environment.

The present disclosure contemplates a computer-readable medium 182 thatincludes instructions 184 or receives and executes instructions 184responsive to a propagated signal; so that a device connected to anetwork 101 can communicate voice, video or data over the network 101.Further, the instructions 184 may be transmitted or received over thenetwork 101 via the network interface device 140.

FIG. 2A is an illustrative view of a building layout for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 2A illustrates a schematic presentation of an example embodiment.In FIG. 2A, an algorithmic framework augments a multi-sensor,multi-modal installation for home-monitoring. The algorithm receivesactivity-specific streams of raw-data captured by the sensors andconstructs a high-level abstraction of the data streams. The resultingabstraction of sensor data is then used to determine individual sensorssalient to accurately capturing ADLs and IADLs. Furthermore, theproposed algorithm identifies pairs of sensors whose correlation orsequence of activity is important to capture ADLs and IADLs. Forinstance, for monitoring a walking behavior from a bedroom to abathroom, the correlation and the sequence of activity of a hallwaysensor and the bathroom door sensor are salient for accurate detectionof the toileting behavior. The hallway sensor and the bathroom doorsensor may be placed in locations specifically intended to detectparticular activities such as movement in the hallway or the bathroomdoor opening or closing. Other examples of activity that may be thesubject of activity-specific streams include a person falling or theperson walking.

The features disclosed herein may be used in the installation phase of amulti-sensor/multi-modal (ambient pressure and motion sensors, visionsensors) home-monitoring system to simulate different user scenarios anddetermine the minimal set of sensors and their placement for optimalmonitoring of user's activities. Furthermore, user-specific factors(e.g., physical build) and specifics of floor plan can be incorporatedin the modelling to identify a more user-aware set of sensors. As such,the features disclosed herein enable tailoring the home-monitoringsolution for each user separately, and in return, help promoting anaccurate and user-aware home-monitoring solution. The proposed salientsensor/channel identification is described in detail in the nextsection. For example, as shown in the illustration on the left in FIG.2A, initial sensors may be placed in various positions around a layout.

Raw sensor data may be obtained and used to determine a minimal numberof sensors and required sensor placement to recognize a particular eventor activity of interest. On the right side of FIG. 2A, the schematicillustrates identified salient sensors and channel pairs (illustratedvia a dashed line, whereby the thickness of the dashed line correspondsto the relative weight or importance of the sensor) for detecting anactivity of interest. By way of example, as illustrated in FIG. 2A, formonitoring a walking behavior from a bedroom to a bathroom, thecorrelation and sequence of activity of a hallway sensor and a bathroomdoor sensor may be salient for accurate detection of washroom behavior.Ambient sensors may include sensors placed around a location, butactivities of daily living may also be monitored using wearable sensors.Examples of ambient sensors that can be used include, but are notlimited to:

-   -   motion sensors such as infrared sensors or SONAR sensors    -   temperature sensors    -   humidity sensors    -   light sensors    -   electricity sensors    -   pressure sensors    -   camera sensors such as security cameras

Motion sensors may be, for example, binary infrared sensors that detecteither the presence or non-presence of an obstruction in a path ofinfrared light. For example, an infrared motion sensor may detect anobject in front of the infrared motion sensor. Motion sensors may, forexample, detect motion of a door opening or closing. Humidity sensorsmay be used, for example, to detect incontinence, or someone taking abath or shower. An electricity sensor may for example, detect applianceusage, such as how long a television is on, how long a light is on,and/or other characteristics of an electrical device. Pressure sensorsmay, for example, detect a pose of a person sleeping when placed in orunder a mattress and may be used to alert nursing staff to adjust apose.

The various sensors described in FIG. 2A may include multi-sensor,multi-modal sensors such as ambient pressure sensors, motion sensors,vision sensors, and/or the like. For example, reference to a firstsensor herein may be to a first group of sensors, and reference to asecond sensor herein may be to a second group of sensors. Each sensor ofa first group of sensors and a second group of sensors may sense thesame type (mode) of characteristics. Additionally, different sensorswithin a first group of sensors and a second group of sensors may sensedifferent types (modes) of characteristics.

In the embodiment of FIG. 2A, the arrangement of sensors may includenumber and location of sensors. Location may be identified bycharacteristics such as other (non-sensor) elements near a sensor suchas doors, windows and drawers. Location may be identified by aparticular type of room or other space in a building such as kitchen,bathroom, hall, basement and so on. Beyond the raw data streams that areused in the determinations of an optimal sensor configuration asdescribed herein, other variables such as location may be used either ina measurement form or a binary form. Measurement forms include, but arenot limited to, e.g., height measurement, weight measurement, BMImeasurement, distance between floor and ceiling, room area, length/widemeasurements of a room, humidity measurements, temperature measurements,and/or the like. Binary forms may include, for example, presence of apet (Y/N), height above/below a threshold (Y/N), seasonal presence suchas whether the current season is summer (Y/N), and/or the like.

In the embodiment of FIG. 2A, optimal sensor/channel set placement canbe identified for a living arrangement to sense ADLs for a user. Thedashed lines on the left indicate the stream of raw sensor data whichare fed into controller described herein, to be subject to an algorithmto identify the optimal sensor arrangement. On the right side of FIG. 2Athe identified salient sensors and channel pairs are shown as dashedlines. The thickness of the dashed lines on the right side indicate theimportance of the corresponding sensor for detecting the activity ofinterest. A solid line between sensors on the right indicates anidentified salient pair-wise correlation for recognizing an activity ofinterest.

An example context for the embodiment of FIG. 2A is sensors placedaround a home. Another example context for the embodiment of FIG. 2A issensors placed around a nursing home.

FIG. 2B is an illustrative view of a model for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 2B illustrates an example model for salient sensor and/or channelidentification that obtains a hybrid generative-discriminativeabstraction of multi-variate time-series observations (sensor datastreams). A model or models as described herein may refer to aprobabilistic graphical model. For example, as illustrated in FIG. 2B,the raw data received via the placed sensors may be encoded asactivity-specific time-series observations in a stochastic model or adynamic Bayesian network (e.g., separate hidden Markov models (HMM)).

In this manner, temporal information of sensor data streams may beembedded as a dynamic stochastic process. Next, the sensor data streamsmay be projected in the posterior space of the resultingactivity-specific models (e.g., HMMs), hence repurposing the models(e.g., HMMs) for a systematic time-series representation. Progressionsthrough each state of an activity sensed by each sensor may beaccumulated and analyzed to determine the probability of any one stateresulting next in the same or another state. Then, distances betweenactivity-specific probability distributions may be computed. Theseresulting probability distances may be aggregated to obtain a sharedprobabilistic representation of the sensor data. Dimensions of theresulting shared probabilistic space may be automatically weightedaccording to their relevance to distinguishing between different eventsand activities using the Group LASSO regression. The Group LASSOregression may apply a multinomial logistic regression model with agroup LASSO penalty or a binomial logistic regression model with a groupLASSO penalty. Additional sensor data may be received from the sensorsfollowing the original modeling, and then applied to determine thelikelihood that any particular model generated the additional sensordata. The determined likelihoods are associated with the first sensorand with the second sensor. And, minimal set of sensors may beidentified as most salient for tracking and detecting events andactivities of interests based on the weights determined. The minimal setof sensors may be minimized group of sensors from the initial sensors,such as a subset of the initial sensors.

This approach illustrated in FIG. 2B may apply the group-LASSO penaltyon the posterior probabilities (or affinity matrix constructed based onsymmetric Kullback-Leibler divergence between posteriors) estimated bythe forward algorithm and hidden Markov models of the multi-variatetime-series observations. In this manner, a multinomial logisticregression model with group LASSO penalty is applied over posteriors ofthe generative networks with each group corresponding to posteriors of asensor. Accordingly, the system may provide a systematic framework tooptimize the number of sensors and their placement needed to capture atarget event, activity and/or phenomenon. And, this methodologyovercomes one of the limitations of conventional feature selectiontechniques where observations need to be of a fixed length, which isproblematic with raw sensor data where in a naturalistic setting, thedata is not typically of a fixed length.

With respect to ambient sensor placement for ADLs, there may be anabundance of multi-modal, multi-variate time-series observations, notall of which are relevant to discriminating between activities ofinterest, related to the sensors used for sensing the ADL activities.The presence of irrelevant time-series data poses several challenges,such as obscuring data streams most salient to activities of interest,impeding predictive and discriminative analysis, and addingcomputational, storage, and maintenance expenses. The systems andmethods described herein, however, identify time-series modalities andchannels salient to discriminating between activities of interest.

As described above for FIG. 2B, stochastic generative-discriminativeencoding of sensor data can be regularized. Event-specific data streamsmay be encoded in a separate generative dynamic Bayesian network (e.g.,hidden Markov model). Multinomial logistic regression with group LASSOpenalty may be performed over posteriors of the generative networks witheach group corresponding to posteriors of a sensor. Details specific tousers living in the living space (e.g., user needs and physical build)can be incorporated in the regression to identify a more user-specificsensor deployment. The approach accounts for the time-series andvariable-length nature of event-specific sensor data streams and can beapplied to any number of dependent or independent sensors in multi-modalsensing settings. Identifying a minimal set of sensors most salient fordetecting ADLs as in FIG. 2B helps promote efficient, low-cost, accurateADL tracking and monitoring technologies. The resulting minimal set ofsensors may be used to optimize sensor deployment for trackingactivities of daily living in an ambient sensing setup such as for homemonitoring.

Another explanation of the flow in FIG. 2B for the salientsensor/channel identification approach is that color-coded groups maycorrespond to sensor-specific variables. Of the sensor-specificvariables, those relevant to the events of interest can be identifiedand returned as reflective of the minimal set of sensors that should beused. The m initial sensors are indicated as S1 to Sm. λ_(m,k) indicatesthe HMM model encoding the kth activity captured through mth sensor. InFIG. 2B, P(Om/λ_(m,k)) is the posterior of the stream data from m^(th)sensor (Om) being generated by λ_(m,k), where posterior is synonymoushere to conditional likelihood or probability. G_(n) is a set of HMIparameters corresponding to the nth sensor.

Ultimately, the output of the flow in FIG. 2B can be used to identifyrelevance of sensors to activities monitored by the sensors. When thesensors sense progression of states reflective of activities sensed bythe sensors, the flow helps identify the relevance of each sensor toeach activity. Thus, when an initial group of sensors includes a firstsensor and a second sensor that monitor a first activity and a secondactivity, the flow in FIG. 2B can be to determine which sensors areincluded and which sensors are excluded from a resultant arrangementused to monitor the space. Thus, of an initial set that includes a firstsensor and a second sensor, one of the first sensor and the secondsensor may be included in an arrangement based on the relevance to theactivities, and the other of the first sensor and the second sensor maybe excluded in the arrangement of sensors based on the relevance to theactivities.

FIG. 2C is an illustrative view of a physiological layout fordetermining an arrangement of sensors, in accordance with arepresentative embodiment.

By way of example, FIG. 2C illustrates obtaining optimal sensorconfigurations for a physiological sensor used in hand gesturerecognition. In this example, hand gestures may be detected fromassociated electromyographic (EMG) activities captured at arm andforearm. In this example, and in physiological sensor configurationgenerally, it may be important to identify how many sensors are requiredto detect specific events and/or activities, as well as where thesensors should be located to help discriminating between differentevents, such as hand gestures. The approach described herein may accountfor stochastic, interpersonal, timing, and phase variations.

Given multi-modal/multi-variate time-series observations, the system andmethods described herein may identify a minimal set of time-seriesvariables/modalities most salient to discriminating between differentclasses of the observations. For example, as illustrated in FIG. 2C, toreduce the number of sensors (1 to 12) illustrated in the topillustration to the sensors defined by the solid blocks (1, 3, 7, 9, and11) in the bottom illustration, the following procedure may be used.

First, multi-variate time-series observations may be encoded into hybridstochastic generative-discriminative models. Then, a sharedprobabilistic representation may be generated where observations arerepresented in terms of pair-wise distances between the stochasticmodels. Parameters of the resulting stochastic models may then beweighted using multinomial logistic regression with group LASSO penaltyin the shared probabilistic space, with each group corresponding tosensor-specific distances (posterior distances between a sensor and therest of the sensors for each activity). And finally, the system maydetermine salient sensor channels based on the weighted parameters, andclassify observation based on the reduced sensor set.

The systems and methods that determine the optimal sensor configurationfor physiological sensing technologies may be used to determine ageneral sensor configuration for a user of a particular height, weight,and/or build, or the systems and methods may be used to determineoptimal sensor placement for a specific user based on specific userfeatures (height, weight, BMI, physiological characteristics, userdiagnosis and/or the like).

Systems and methods described herein may not only determine the numberand placement of sensors but may also determine a sensor type (ECG, PPG,accelerometer, gyroscope, and/or the like).

Similar to the ambient sensing technologies optimal placement, themethods described herein for physiological sensing technologyconfigurations may include the methods described above with respect toFIG. 2B. Using the methodology of FIG. 2B for physiological sensingtechnologies, various constraints specific to the ambient sensingtechnologies may be imposed. For example, constraints may be specifiedto ensure members of a set of sensors are always selected together(e.g., the involvement of both the flexor pollicis longus muscle and theflexor digitorum superficialis muscle to flex the thumb and bend fingersin making a first gesture). As such, the proposed approach will beconstrained to (de)-select the interacting channels together based ontheir collective saliency to the classification task.

A priori information about sensor combinations may include details of ahousehold arrangement, such as if a user lives with a pet. This kind ofinformation that is particular to individual households can be used toarrange the initial set of sensors. For example, if the user lives witha pet, detection of the user's motion in the hallway may require motionsensors at heights of 1 foot and 5 feet to both activate simultaneouslysince activation of the sensor at 1 foot may be triggered solely by thepet. If the a priori information about sensor combinations is available,the approach illustrated in FIG. 2B can incorporate the a prioriinformation to (de)select a combination of sensors (e.g., a sensortriplet) based on their collective saliency to detecting ADLs.Furthermore, the approach can incorporate information about user'sphysical build (e.g., height), to identify a more user-aware set ofsensors and their deployment.

If information about sensor combinations and/or environment is availableprior to final configuration, those data points may be included in thecalculation process. By way of example, if a user lives with a pet, todetect user's motion in the hallway, two motion sensors may be needed,one at a low height (e.g., one foot off the ground) and a second at ahigher height (e.g., five feet off the ground) whereby the sensors maybe required to be activated simultaneously.

Where initial raw sensor data is unavailable, image layouts, user data,and/or other known data may be used to model sensor placement, wherebythe raw sensor data may be simulated using the image layouts, user data,and/or other known data sources.

FIG.3A is an illustrative view of a controller for determining anarrangement of sensors, in accordance with a representative embodiment.

In FIG. 3A, a controller 380 includes a memory 330 that storesinstructions and a processor 320 that executes the instructions. Thecontroller 380 may be provided in a variety of devices, system andarrangements, including a mobile computer or tablet. The processor 320may execute the instructions to implement part or all of methodsdescribed herein. Additionally, the controller 380 may be distributedamong several devices, such as when methods are necessarily implementedin a distributed manner that requires multiples sets of memory/processorcombinations.

FIG. 3B is an illustrative view of a system for determining anarrangement of sensors, in accordance with a representative embodiment.

The system that executes the methods and/or models described herein mayinclude, for example, a system 300 of hardware components as illustratedin FIG. 3B. For example, the system 300 may be a device such as a hostdevice. As shown, the system 300 may include a processor 320, memory330, user interface 340, communication interface 350, and storage 360interconnected via one or more system buses 310. It will be understoodthat FIG. 3B constitutes, in some respects, an abstraction and that theactual organization of the components of the system 300 may be morecomplex than illustrated.

The processor 320 may be any hardware device capable of executinginstructions stored in memory 330 or storage 360 or otherwise processingdata. As such, the processor may include a microprocessor, fieldprogrammable gate array (FPGA), application-specific integrated circuit(ASIC), or other similar devices.

The memory 330 may include various memories such as, for example L1, L2,or L3 cache or system memory. As such, the memory 330 may include staticrandom-access memory (SRAM), dynamic RAM (DRAM), flash memory, read onlymemory (ROM), or other similar memory devices. It will be apparent that,in embodiments where the processor includes one or more ASICs (or otherprocessing devices) that implement one or more of the functionsdescribed herein in hardware, the software described as corresponding tosuch functionality in other embodiments may be omitted.

The user interface 340 may include one or more devices for enablingcommunication with a user such as an administrator, a clinician, atechnician, a user, and/or a doctor. For example, the user interface 340may include a display, a mouse, and a keyboard for receiving usercommands. In some embodiments, the user interface 340 may include acommand line interface or graphical user interface that may be presentedto a remote terminal via the communication interface 350.

The communication interface 350 may include one or more devices forenabling communication with other hardware devices. For example, thecommunication interface 350 may include a network interface card (NIC)configured to communicate according to the Ethernet protocol.Additionally, the communication interface 350 may implement a TCP/IPstack for communication according to the TCP/IP protocols. Variousalternative or additional hardware or configurations for thecommunication interface 350 will be apparent.

The storage 360 may include one or more machine-readable storage mediasuch as read-only memory (ROM), random-access memory (RAM), magneticdisk storage media, optical storage media, flash-memory devices, orsimilar storage media. In various embodiments, the storage 360 may storeinstructions for execution by the processor 320 or data upon with theprocessor 320 may operate. For example, the storage 360 may store dataassociated with ambient sensing technologies, physiological sensingtechnologies (e.g., raw sensor data), data associated with any stage ofthe methodologies described herein including, for example, data fromtime-series encoding, probability distribution distance calculation,shared probabilities representations, and group LASSO optimizations.Where the system 300 implements the procedures as described herein, thestorage 360 may include an operating system 361, a time series encodingengine 362, a probability distribution distances engine 363, a sharedprobabilistic representation engine 364, and a group LASSO 365. The timeseries encoding engine 362 may store executable instructions forencoding activity-specific time-series observations. The probabilitydistribution distances engine 363 may store executable instructions forprojecting sensor data streams in posterior space of resultingactivity/event-specific models and computing distances betweenactivity-specific probability distributions. The shared probabilisticrepresentation engine 364 may store executable instructions foraggregating resulting probability distances to obtain sharedprobabilistic representations of sensor data. The group LASSO 365 maystore executable instructions for weighting dimensions of resultingshared probabilistic spaces according to their relevance to distinguishbetween different events and activities, and for identifying a minimalset of sensors as most salient for tracking and detecting events oractivities of interest. The storage 360 may store additional softwarecomponents required to execute the functionality described herein, whichalso may control operations of the system 300.

It will be apparent that various information described as stored in thestorage 360 may be additionally or alternatively stored in the memory330. In this respect, the memory 330 may also be considered toconstitute a “storage device” and the storage 360 may be considered a“memory.” Various other arrangements will be apparent. Further, thememory 330 and storage 360 may both be considered to be “non-transitorymachine-readable media.” As used herein, the term “non-transitory” willbe understood to exclude transitory signals but to include all forms ofstorage, including both volatile and non-volatile memories.

While the system 300 is shown as including one of each describedcomponent, the various components may be duplicated in variousembodiments. For example, the processor 320 may include multiplemicroprocessors that are configured to independently execute the methodsdescribed herein or are configured to perform steps or subroutines ofthe methods described herein such that the multiple processors cooperateto achieve the functionality described herein. Further, where the system300 is implemented in a cloud computing system, the various hardwarecomponents may belong to separate physical systems. For example, theprocessor 320 may include a first processor in a first server and asecond processor in a second server.

FIG. 4 illustrates a process for determining an arrangement of sensors,in accordance with a representative embodiment.

FIG. 4 illustrates a method 400 that may be executed by the varioussystems described herein. Method 400 may start at block S402. At blockS404 initial sensors may be placed (such as the sensor placement in FIG.2A and/or FIG. 2C). The sensors placed at S404 may be considered a firstset of sensors. Method 400 may not require initial sensor placement butmay require initial data input such as a floorplan and/or roomconfiguration data (e.g., ceiling height, distance between objects,distance between walls, height of door frames, position of lightingfixtures, and/or the like) and/or user data and/or characteristics(e.g., height, weight, BMI, user build data, user vital sign data, userskin characteristics, user diet and/or nutrition information, and/or thelike).

At block S405, the first set of sensors sense raw sensor data for statesof activities. The activities are typically human activities sensed bythe sensors. Examples of activities are moving, breathing, stepping,talking, falling, opening or closing a door, opening or closing awindow, climbing up or down stairs, opening or closing a refrigerator orcabinet, and so on. Examples of sensing including visualizing, hearing,smelling (e.g., detecting a chemical), and feeling (e.g., detecting achange in pressure), and the activity will be understood based on thetype and placement of any sensor as well as what is sensed. The statesof activities reflect differences in statuses of the sensors, such as,for example, changes from quiet to noisy, dark to light, off to on, orother binary changes in states of activities. Therefore, the states area progression of states over time, such as State1 at Time1, Statel atTime2, State1 at Time3, State2 at Time4, and so on. Additionally, thestates are not limited to binary possibilities, as sensors may beconfigured to detect more than two states such as ten or one hundredlevels or a continuous observation (rather than discrete) of sound orlight.

At block S406, a system may receive activity-specific streams of rawdata captured by sensors and/or model activity-specific streams of rawdata. The activity-specific streams of raw data are based on theprogression of states of activity sensed by the sensor over time. Forexample, a sensor may sense dozens, hundreds, thousands or millions ofstates of activities over time and send the detected states as raw datacontinuously, in batches, or on-demand when requested.

At block S407A, time-series observations of the progression of statesare identified using separate models for the raw data for eachactivity-specific stream from each of the first set of sensors. That is,each of the sensors placed at block S404 may source raw data, and aseparate model may be applied for each activity sensed by each sensor.Thus, when a first sensor senses a progression of states for a firstactivity and a progression of states for a second activity, separatemodels may be applied for each of the first activity and the secondactivity sensed by the first sensor. When a second sensor senses aprogression of states for the same first activity and a progression ofstates for the same second activity, additional separate models may beapplied for each of the first activity and the second activity sensed bythe second sensor. The models may be the same model with the same ordifferent input settings, so that the time-series progressions of statesfor each activity-specific stream for (from) different sensors arecomparable in that they are modeled by the same type of model even ifdifferent input settings are used for different activity-specificstreams.

At block S407B, the time-series observations of progressions of statesmay be analyzed to determine a relative amount/volume/number ofprogressions from any one state to any other state for eachactivity-specific time-series. That is, given a number of progressionsfrom state to state in any one time-series of observations, the analysismay be to see the number of times each stays the same or transits toeach other possible state. The determination of the number ofprogressions from one state to any other state can be performedinitially as a count, and then as a comparison that will show whichtransitions from a state are most likely and the likelihood of any onestate transiting to any next state (i.e., the same state or anydifferent state). As noted, the analysis at S407B may be performed foreach time-series observation of states for each activity sensed by eachsensor.

At block S408, a system may encode activity-specific time-seriesobservations in separate models so as to generate encodedactivity-specific time-series observations in the separateactivity-specific models. For example, activity-specific time-seriesobservations may be encoded in separate hidden Markov models (HMM),thereby directly embedding the temporal information of sensor datastreams as a dynamic stochastic process. Hidden Markov model is aBayesian network with discrete hidden states and output units that mayrepresent both discrete and continuous observations. Continuous HMMs mayencode sequential observations as a stochastic process whose dynamics isdescribed by the hidden state variable varying between N hidden statevalues. The transition between hidden state values may be governed bytransition probabilities represented by an N×N state transition matrix.The observable layer of HMM may consist of output units each associatedwith a hidden state value. HMMs may model multivariate observations of Mchannels. The distribution of outputs at each hidden state may bemodeled using a mixture of Gaussian distributions. The mixture ofGaussian outputs may be well-suited for cases with multiple within-classmodes of observations.

A left-to-right HMM configuration may be used to account for a lack ofcyclic movements and other data points that often progress from astarting point to an ending point. Given N multivariate time-seriesobservations (x₁ to x_(N)) each belonging to one of K classes,class-specific HMMs are trained for every channel m∈1, M; λ_(MK): HMMmodel trained on m^(th) channel for k^(th) class.

At block S409, the probability for each state of each activityprogressing next to each state for the same activity is determined. Inthis way, a transition between one state and the next is determined as aprobability of progressing from one state to the next for each possiblenext state. The probability determined at S409 is determined for eachactivity sensed by each sensor.

At block S410, a system may project sensor data streams in posteriorspace of resulting activity-specific models. For example, for everyobservation O sensed by mth sensor, the probabilities that theobservation is generated by the class-specific models (λ_(mk), k=1, . .. K) are computed (posterior probability: P (O|λ_(mk)) using the forwardalgorithm. The projection is a K-dimensional projection with eachdimension corresponding to a probability distribution associated with aclass, and the probability distributions for each activity sensed byeach sensor can therefore be subject to comparisons given the projectiononto K dimensions.

At block S412, a system may compute distances between activity-specificprobability distributions. For example, pair-wise symmetricKullback-Leibler (KL) distances between the resulting posteriors basedon divergence may be used as the shared probabilistic representation ofthe observations.

At block S414, a system may aggregate resulting probability distances toobtain shared probabilistic representations of sensor data.

At block S416, a system may weight dimensions of resulting sharedprobabilistic spaces according to their relevance to distinguish betweendifferent events and/or activities. For example, groupingchannel-specific distances, group Lasso regression may then be performedin the resulting shared probabilistic space. Group Lasso, for example,may suppose there are G groups of features, each including K_(g)members. A group Lasso optimization may be formulated as

$\begin{matrix}{{\min\limits_{\beta_{1},\ldots,\beta_{G}}{{y - {\sum\limits_{g = 1}^{G}\; {\beta_{g}^{T}X_{g}}}}}^{2}},{{{s.t.\text{:}}\mspace{14mu} {\sum\limits_{g = 1}^{G}\; \sqrt{\beta_{g}^{T}\beta_{g}}}} \leq s},} & (1)\end{matrix}$

-   -   where Xg is the representation of independent variables over a        collection of features in group g, β_(g) carries the        corresponding coefficients for the individual members of the        group g, y is the response variable, and s is a constant        defining the upper bound on the sparsity constraint. Introducing        Lagrange multiplier γ, the resulting group Lasso minimization        can be rewritten as.

$\begin{matrix}{{\min\limits_{\beta_{1},\ldots,\beta_{G}}{{y - {\sum\limits_{g = 1}^{G}\; {\beta_{g}^{T}X_{g}}}}}^{2}} + {\gamma {\sum\limits_{g = 1}^{G}\; {{\beta_{g}}.}}}} & (2)\end{matrix}$

At block S418, a system may identify a minimal set of sensors mostsalient for tracking and detecting events and/or activities of interest.For example, selected groups of distances may correspond to channelsmost salient to discriminating between time-series observations.

At block S422, the method may end.

It is further noted that the systems and methods described herein may betangibly embodied in one of more physical media, such as, but notlimited to, a compact disc (CD), a digital versatile disc (DVD), afloppy disk, a hard drive, read only memory (ROM), random access memory(RAM), as well as other physical media capable of storing software, orcombinations thereof. Moreover, the figures illustrate variouscomponents (e.g., servers, computers, processors, etc.) separately. Thefunctions described as being performed at various components may beperformed at other components, and the various components bay becombined or separated. Other modifications also may be made.

FIG. 5 illustrates another process for determining an arrangement ofsensors, in accordance with a representative embodiment.

In the embodiment of FIG. 5, initial sensors are placed at S504.

At S506, activity-specific streams of raw sensor data captured by theinitial sensors may be received.

At S508, encoded time-series of observations of different activities maybe generated based on the raw sensor data using separate models.

At S510, the encoded time-series of observations may be projected inposterior spaces of the separate models to obtain the probabilitydistributions of the different activities sensed by the differentsensors.

At S512, the probability distances between probability distributions ofthe first activity and the second activity may be determined.

At S514, probability distances may be aggregated to obtain sharedprobabilistic space representative of the time-series of observationsactivities based on raw sensor data.

At S516, dimensions of the resulting shared probabilistic space may beweighted according to their relevance to distinguish between differentevents and activities.

At S518, a minimal resultant group of sensors most salient for trackingand detecting events/activities of interest may be identified. Theminimal resultant group identified at S518 may be a subset of theinitial sensors. At S520, the resultant sensors may be arranged. Thearranging at S520 may be by a technician such as by removing one or moreof the initial sensors, moving one or more of the initial sensors,and/or adding a new sensor to the initial sensors. The arranging at S520will typically result in fewer sensors than the initial sensors.Additionally, the arranging at S520 may be performed by simply remotelydeactivating one or more of the initial sensors that are already inplace. As a result, the sensor required for an arrangement is reduced,and the sensor streams being monitored is reduced to those salient fortracking a user's activities of interest. Thus, the minimal resultantgroup is optimized in terms of location (e.g., locations in a home) aswell as in number based on the process of FIG. 5 as well as for otherembodiments described herein.

FIG. 6 illustrates another process for determining an arrangement ofsensors, in accordance with a representative embodiment.

In FIG. 6, the process starts at S656A by receiving, from a firstsensor, first sensor data including time-series observation representinga first activity and a second activity. At S656B, second sensor data maybe received from a second sensor. The second sensor data includestime-series observation representing the first activity and the secondactivity. The operations at S656A and S656B may be similar or identicalto the operations at S506 in FIG. 5.

At S658A, a first model for first activity is generated. The firstactivity, as is the case with most or all activity described herein,involves a progression through states sensed by a sensor, in this caseby the first sensor. At S658B, a second model for second activity isgenerated. The second activity may also involves a progression throughstates sensed by a sensor, in this case also the first sensor. At S658C,a third model for the first activity may be generated. The firstactivity now involves a progression through states sensed by the secondsensor. At S658D, a fourth model for the second activity may begenerated. The second activity also now involves a progression throughstates sensed by the second sensor.

At S659A, third sensor data is received from the first sensor. The thirdsensor data includes time-series observation again representing thefirst activity and the second activity. At S659B, fourth sensor data isreceived from the second sensor. The fourth sensor data also includestime-series observation again representing the first activity and thesecond activity.

At S660, the likelihood that the first model generated the third sensordata is determined. The likelihood that the second model generated thethird sensor data may also be determined. The likelihood that the thirdmodel generated the fourth sensor data may be determined. The likelihoodthat the fourth model generated the fourth sensor data may also bedetermined. Here, the first model, the second model, the third model,and the fourth model may be used to ultimately determine theireffectiveness (e.g., relevancy) in capturing first activity and secondactivity after the models are generated.

At S662, pair-wise distance between each sensor-specific likelihood maybe calculated to obtain calculated distances.

At S664, calculated distances for likelihoods involving the first sensormay be grouped, and calculated distances for likelihoods involving thesecond sensor may be grouped. As a result of the grouping at S664,grouped calculated distances may be obtained.

At S666, the process may determine a first relevance of the first sensorand a second relevance of the second sensor for capturing first activityand second activity. The first relevance and the second relevance may bedetermined by executing a regression model using the grouped calculateddistances in order to determine the effectiveness of each sensor incapturing the activities of interest.

In FIG. 6, the process can be used to identify an arrangement of sensorsfor monitoring a space. The arrangement can be identified by and definedby characteristics of the space, which in turn may be input to themodeling in FIG. 6 as a priori settings, and which can also be reflectedby the data streams of raw data output from the sensors. The space maybe enclosed, such as a living space for a user, such as house orapartment with different rooms primarily used for different purposes.

As an example, a sensor may not be as relevant to an activity if thesensor is too far away from the activity, if a physical obstruction isbetween the sensor and the activity, if the sensor is pointed in adirection away from the activity, or if the sensor simply does not sensea characteristic of the activity that can be sensed (e.g., a noisesensor may not sense a quiet movement).

The process of FIG. 6 may coincide with or include features of theprocess of FIG. 4 and the process of FIG. 5. For example, identifying aminimized subset of sensors from the initial set of sensors as in S418and S518 may be performed to coincide with or result from determiningthe relevance of sensors as in S666. Additionally, arranging theresultant group of sensors (i.e., the minimized subset) as in S520 maybe performed as a result of determining the relevance of sensors as inS666.

Additionally, the relevance of the sensors determined at S666 isreflective of their utility in observing activities of interest. Thus,the likelihoods of the different models generating the newer sensor datais useful in determining the relevance of the sensors that generate thenewer sensor data. As a result, whether a sensor is included or excludedin the determined arrangement following the process of FIG. 6 is tied tothe likelihood of the models corresponding to the sensor actuallygenerating the newer sensor data after the models are generated.

FIG. 7 is an illustrative view of another system for determining anarrangement of sensors, in accordance with a representative embodiment.

FIG. 7 depicts a system 700 used in determining an optimal sensorconfiguration for ambient and/or physiological sensing technologies. Thesystem 700 in FIG. 7 may enable a backend system, such as the system 300for example, to provide network services to users associated with userdevices, such as mobile and/or client devices that may communicate witha backend system. As shown in FIG. 7, the system 700 may include a userdevice 702, a network 704, a front-end controlled domain 706, a back-endcontrolled domain 712, and a backend 718. Front-end controlled domain706 may include one or more load balancer(s) 708 and one or more webserver(s) 710. Back-end controlled domain 712 may include one or moreload balancer(s) 714 and one or more application server(s) 716.

The user device 702 may be a network-enabled computer such as a clientdevice. As referred to herein, a network-enabled computer may include,but is not limited to: e.g., any computer device, or communicationsdevice including, e.g., a server, a network appliance, a personalcomputer (PC), a workstation, a mobile device, a phone, a handheld PC, apersonal digital assistant (PDA), a thin client, a fat client, anInternet browser, or other device. The one or more network-enabledcomputers of the system 700 may execute one or more softwareapplications to enable, for example, network communications.

User device 702 also may be a mobile device. For example, a mobiledevice may include an iPhone, iPod, iPad from Apple® or any other mobiledevice running Apple's iOS operating system, any device running Google'sAndroid® operating system, including for example, Google's wearabledevice, Google Glass, any device running Microsoft's Windows® Mobileoperating system, and/or any other smartphone or like wearable mobiledevice.

Network 704 may be one or more of a wireless network, a wired network,or any combination of a wireless network and a wired network. Forexample, network 704 may include one or more of a fiber optics network,a passive optical network, a cable network, an Internet network, asatellite network, a wireless LAN, a Global System for MobileCommunication (GSM), a Personal Communication Service (PCS), a PersonalArea Networks, (PAN), D-AMPS, Wi-Fi, Fixed Wireless Data, IEEE 802.11b,802.15.1, 802.11n, and 802.11g or any other wired or wireless networkfor transmitting and receiving a data signal.

In addition, network 704 may include, without limitation, telephonelines, fiber optics, IEEE Ethernet 902.3, a wide area network (WAN), alocal area network (LAN) or a global network such as the Internet. Also,network 704 may support an Internet network, a wireless communicationnetwork, a cellular network, or the like, or any combination thereof.Network 704 may further include one network, or any number of exampletypes of networks mentioned above, operating as a stand-alone network orin cooperation with each other. Network 704 may utilize one or moreprotocols of one or more network elements to which they arecommunicatively couples. Network 704 may translate to or from otherprotocols to one or more protocols of network devices. Although network704 is depicted as a single network, it should be appreciated thataccording to one or more embodiments, network 704 may include multipleinterconnected networks, such as, for example, the Internet, a serviceprovider's network, a cable television network, corporate networks, andhome networks.

Front-end controlled domain 706 may be implemented to provide securityfor backend 718. Load balancer(s) 708 may distribute workloads acrossmultiple computing resources, such as, for example computers, a computercluster, network links, central processing units or disk drives. Invarious embodiments, load balancer(s) 708 may distribute workloadsacross, for example, web server(s) 710. Load balancing aims to optimizeresource use, maximize throughput, minimize response time, and avoidoverload of any one of the resources. Using multiple components withload balancing instead of a single component may increase reliabilitythrough redundancy. Load balancing is usually provided by dedicatedsoftware or hardware, such as a multilayer switch or a Domain NameSystem (DNS) server process.

Load balancer(s) 708 may include software that monitoring the port whereexternal clients, such as, for example, user device 702, connect toaccess various services of a backend system, for example. Loadbalancer(s) 708 may forward requests to one of the application server(s)716 and/or backend 718 servers, which may then reply to load balancer(s)708. This may allow load balancer(s) 708 to reply to user device 702without user device 702 ever knowing about the internal separation offunctions. It also may prevent user devices from contacting backendservers directly, which may have security benefits by hiding thestructure of the internal network and preventing attacks on backend 718or unrelated services running on other ports, for example.

A variety of scheduling algorithms may be used by load balancer(s) 708to determine which backend server to send a request to. Simplealgorithms may include, for example, random choice or round robin. Loadbalancer(s) 708 also may account for additional factors, such as aserver's reported load, recent response times, up/down status(determined by a monitoring poll of some kind), number of activeconnections, geographic location, capabilities, or how much traffic ithas recently been assigned.

Load balancer(s) 708 may be implemented in hardware and/or software.Load balancer(s) 708 may implement numerous features, including, withoutlimitation: asymmetric loading; Priority activation: SSL Offload andAcceleration; Distributed Denial of Service (DDoS) attack protection;HTTP/HTTPS compression; TCP offloading; TCP buffering; direct serverreturn; health checking; HTTP/HTTPS caching; content filtering;HTTP/HTTPS security; priority queuing; rate shaping; content-awareswitching; client authentication; programmatic traffic manipulation;firewall; intrusion prevention systems.

Web server(s) 710 may include hardware (e.g., one or more computers)and/or software (e.g., one or more applications) that deliver webcontent that can be accessed by, for example a client device (e.g., userdevice 702) through a network (e.g., network 704), such as the Internet.In various examples, web servers, may deliver web pages, relating to,for example, sensor configuration optimization and the like, to clients(e.g., user device 702). Web server(s) 710 may use, for example, ahypertext transfer protocol (HTTP/HTTPS or sHTTP) to communicate withuser device 702. The web pages delivered to client device may include,for example, HTML documents, which may include images, style sheets andscripts in addition to text content.

A user agent, such as, for example, a web browser, web crawler, ornative mobile application, may initiate communication by making arequest for a specific resource using HTTP/HTTPS and web server(s) 710may respond with the content of that resource or an error message ifunable to do so. The resource may be, for example a file stored onbackend 718. Web server(s) 710 also may enable or facilitate receivingcontent from user device 702 so user device 702 may be able to, forexample, submit web forms, including uploading of files.

Web server(s) also may support server-side scripting using, for example,Active Server Pages (ASP), PHP, or other scripting languages.Accordingly, the behavior of web server(s) 710 can be scripted inseparate files, while the actual server software remains unchanged.

Load balancer(s) 714 may be similar to load balancer(s) 708 as describedabove and may distribute workloads across application server(s) 716 andbackend 718 server(s).

Application server(s) 716 may include hardware and/or software that isdedicated to the efficient execution of procedures (e.g., programs,routines, scripts) for supporting its applied applications. Applicationserver(s) 716 may include one or more application server frameworks,including, for example, Java application servers (e.g., Java platform,Enterprise Edition (Java EE), the .NET framework from Microsoft®, PHPapplication servers, and the like). The various application serverframeworks may contain a comprehensive service layer model. Also,application server(s) 716 may act as a set of components accessible to,for example, the system 700 that implements entities, through an APIdefined by the platform itself. For Web applications, these componentsmay be performed in, for example, the same running environment as webserver(s) 710, and application server(s) 716 may support theconstruction of dynamic pages. Application server(s) 716 also mayimplement services, such as, for example, clustering, fail-over, andload-balancing. In various embodiments, where application server(s) 716are Java application servers, the application server(s) 716 may behavelike an extended virtual machine for running applications, transparentlyhandling connections to databases associated with backend 718 on oneside, and, connections to the Web client (e.g., the user device 702) onthe other.

Backend 718 may include hardware and/or software that enables thebackend services of, for example, an entity that maintains a distributedsystem similar to the system 700. For example, backend 718 may include,a system capable of performing the methods disclosed herein, such asmethod 400 for example. Backend 718 may be associated with variousdatabases. Backend 718 also may be associated with one or more serversthat enable the various services provided by the system 700.

As described above, optimal sensor placement may be determined forambient sensing technologies, physiological sensing technologies, and/orany combination thereof. The optimization of sensor placement andarrangement can be used to augment any multi-sensor, multi-modaldistributed tracking technologies, including those relating to ambientmonitoring of daily activities, as well as physiology (e.g., body areanetworks and patient monitoring).

For instance, in an ambient health monitoring setup, it is often unclearwhat type of sensor, how many of them and where in the room, should bedeployed to enable seamless observation of user's ADLs and IADLs.Therefore, it is desired to identify the modalities and sensors salientto activities and events of interest (e.g. number and placement ofmotion sensors for tracking sleep in a bedroom). Even with a predefinednumber of sensors, a proper deployment of the sensors is critical forthe optimal monitoring performance of an ambient sensing system.

As described above, relevance of sensors (e.g., a first sensor and asecond sensor) to activities (e.g., a first activity and a secondactivity) can be identified by executing a regression model usinggrouped calculated probabilistic distances. The relevance can beidentified even when representation of the observations isvariable-length as is expected for sensor data collected in anaturalistic setting. Differences in sequence order of data primitives(states sensed by sensors), timing, phase, and other characteristicsreflective of actual behavior can be accounted for in the optimizationdescribed herein. The optimal sensor deployment described herein alsopromotes less intrusive ambient sensing technologies by helping toreduce the number of required sensors to only those most salient fordetecting the activity of interest. Therefore, the systematic approachdescribed herein can help promote efficient, inexpensive, and accuratehealth-enabling and home-monitoring solutions.

Optimal sensor placement is also applicable to many other types ofcontexts in which multiple sensors are used, and optimization of thesensors may provide benefits. For example, in a utility system such asan electric network or water distribution system, different types ofsensors may be placed in many different locations to monitorcharacteristics of the utility system. The number of sensors can beminimized by using the processing described herein to identify whichsensors and which locations are salient to the activities beingmonitored, and thus the information being sought.

In another example, a complex industrial system may include differenttypes of sensors placed in different locations to monitor differentcharacteristics of the industrial system. The number and type of sensorsbeing used may be optimized by starting with an excess of sensors placedin varying locations, and then performing the processing describedherein to identify the minimal set of sensors salient to detect theactivities being monitored and/or desired to be monitored. For example,mechanical motions of a component of an industrial system beingmonitored may be identified by a minimized set of one or more sensorsselected according to the processing described herein.

Moreover, the cost of particular types of sensors can be taken intoaccount in the processing, in that the saliency of sensors to monitoringdifferent activities may vary based on the cost of the sensors.Accordingly, post-processing after the group LASSO optimizationdescribed herein can be used to adjust the results of regressionanalysis based on the relative cost of identified sensors relative toother sensors determined to otherwise be less than optimal.

As described above, the present disclosure is not to be limited in termsof the particular embodiments described in this application, which areintended as illustrations of various aspects. Many modifications andvariations can be made without departing from its spirit and scope, asmay be apparent. Functionally equivalent methods and apparatuses withinthe scope of the disclosure, in addition to those enumerated herein, maybe apparent from the foregoing representative descriptions. Suchmodifications and variations are intended to fall within the scope ofthe appended representative claims. The present disclosure is to belimited only by the terms of the appended representative claims, alongwith the full scope of equivalents to which such representative claimsare entitled. It is also to be understood that the terminology usedherein is for the purpose of describing particular embodiments only andis not intended to be limiting.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It may be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It may be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent may be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations. In addition, even if a specificnumber of an introduced claim recitation is explicitly recited, suchrecitation should be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, means at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” is used, in general such a constructionis intended in the sense one having skill in the art would understandthe convention (e.g., “a system having at least one of A, B, and C”would include but not be limited to systems that have A alone, B alone,C alone, A and B together, A and C together, B and C together, and/or A,B, and C together, etc.). In those instances where a conventionanalogous to “at least one of A, B, or C, etc.” is used, in general sucha construction is intended in the sense one having skill in the artwould understand the convention (e.g., “ a system having at least one ofA, B, or C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It may be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” may be understood toinclude the possibilities of “A” or “B” or “A and B.”

The foregoing description, along with its associated embodiments, hasbeen presented for purposes of illustration only. It is not exhaustiveand does not limit the concepts disclosed herein to their precise formdisclosed. Those skilled in the art may appreciate from the foregoingdescription that modifications and variations are possible in light ofthe above teachings or may be acquired from practicing the disclosedembodiments. For example, the steps described need not be performed inthe same sequence discussed or with the same degree of separation.Likewise various steps may be omitted, repeated, or combined, asnecessary, to achieve the same or similar objectives. Accordingly, thepresent disclosure is not limited to the above-described embodiments,but instead is defined by the appended claims in light of their fullscope of equivalents.

In the preceding specification, various preferred embodiments have beendescribed with references to the accompanying drawings. It may, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the inventive concepts disclosed herein as setforth in the claims that follow. The specification and drawings areaccordingly to be regarded as an illustrative rather than restrictivesense.

Although system and method of optimal sensor placement has beendescribed with reference to several exemplary embodiments, it isunderstood that the words that have been used are words of descriptionand illustration, rather than words of limitation. Changes may be madewithin the purview of the appended claims, as presently stated and asamended, without departing from the scope and spirit of system andmethod of optimal sensor placement in its aspects. Although system andmethod of optimal sensor placement has been described with reference toparticular means, materials and embodiments, system and method ofoptimal sensor placement is not intended to be limited to theparticulars disclosed; rather system and method of optimal sensorplacement extends to all functionally equivalent structures, methods,and uses such as are within the scope of the appended claims.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of the disclosuredescribed herein. Many other embodiments may be apparent to those ofskill in the art upon reviewing the disclosure. Other embodiments may beutilized and derived from the disclosure, such that structural andlogical substitutions and changes may be made without departing from thescope of the disclosure. Additionally, the illustrations are merelyrepresentational and may not be drawn to scale. Certain proportionswithin the illustrations may be exaggerated, while other proportions maybe minimized. Accordingly, the disclosure and the figures are to beregarded as illustrative rather than restrictive.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to practice the concepts describedin the present disclosure. As such, the above disclosed subject matteris to be considered illustrative, and not restrictive, and the appendedclaims are intended to cover all such modifications, enhancements, andother embodiments which fall within the true spirit and scope of thepresent disclosure. Thus, to the maximum extent allowed by law, thescope of the present disclosure is to be determined by the broadestpermissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

What is claimed is:
 1. A controller for determining an arrangement ofsensors for monitoring a space, comprising: a memory that storesinstructions; and a processor that executes the instructions, wherein,when executed by the processor, the instructions cause the controller toexecute a process comprising: receiving, from a first sensor of at leasttwo sensors, a first sensor data comprising at least one time-seriesobservation representing at least a first activity and a secondactivity; receiving, from a second sensor of the at least two sensors, asecond sensor data comprising at least one time-series observationrepresenting the first activity and the second activity; generating, bythe processor, a first model for the first activity involving a firstprogression through a plurality of states indicated by at least aportion of the first sensor data; generating, by the processor, a secondmodel for the second activity involving a second progression through aplurality of states indicated by at least a portion of the first sensordata; generating, by the processor, a third model for the first activityinvolving a third progression through a plurality of states indicated byat least a portion of the second sensor data; generating, by theprocessor, a fourth model for the second activity involving a fourthprogression through a plurality of states indicated by at least aportion of the second sensor data; receiving, from the first sensor, athird sensor data comprising at least one time-series observationrepresenting at least the first activity and the second activity;receiving, from the second sensor, a fourth sensor data comprising atleast one time-series observation representing at least the firstactivity and the second activity; determining, using the processor, alikelihood that the first model generated at least a portion of thethird sensor data, a likelihood that the second model generated at leasta portion of the third sensor data, a likelihood that the third modelgenerated at least a portion of the fourth sensor data, and a likelihoodthat the fourth model generated at least a portion of the fourth sensordata; calculating, using the processor, a pair-wise distance betweeneach sensor-specific determined likelihood to obtain calculateddistances; grouping, using the processor, the calculated distances forthe likelihoods involving the first sensor, and grouping, using theprocessor, the calculated distances for the likelihoods involving thesecond sensor, to obtain grouped calculated distances, and determining,using the processor, a first relevance of the first sensor and a secondrelevance of the second sensor for capturing the first activity and thesecond activity by executing a regression model using the groupedcalculated distances, wherein one of the first sensor and the secondsensor is included in an arrangement of sensors used to monitor thespace based on the first relevance and the second relevance determinedusing the processor and the other of the first sensor and the secondsensor is excluded in the arrangement of sensors used to monitor thespace based on the first relevance and the second relevance determinedusing the processor.
 2. The controller of claim 1, wherein the processexecuted by the controller further comprises: performing one of:activating the first sensor based on the first relevance or removing thefirst sensor based on the first relevance.
 3. The controller of claim 1,wherein the process executed by the controller further comprises:performing one of: activating the second sensor based on the secondrelevance or removing the second sensor based on the second relevance.4. The controller of claim 1, wherein the process executed by thecontroller further comprises: receiving, from a third sensor of the atleast two sensors, a fifth sensor data comprising at least onetime-series observation representing the first activity and the secondactivity; generating, by the processor, a fifth model for the firstactivity involving a fifth progression through a plurality of statesindicated by at least a portion of the fifth sensor data; generating, bythe processor, a sixth model for the second activity involving a sixthprogression through a plurality of states indicated by at least aportion of the fifth sensor data; receiving, from the third sensor, asixth sensor data comprising at least one time-series observationrepresenting at least the first activity and the second activity; anddetermining, using the processor, a likelihood that the fifth modelgenerated at least a portion of the sixth sensor data, and a likelihoodthat the sixth model generated at least a portion of the sixth sensordata.
 5. The controller of claim 1, wherein the first sensor comprises afirst group of sensors, and wherein the second sensor comprises a secondgroup of sensors.
 6. The controller of claim 1, wherein the first sensordata comprises a first time-series observation representing a firstactivity and a second time-series observation representing a secondactivity.
 7. The controller of claim 1, wherein the first modelcomprises a probabilistic graphical model.
 8. The controller of claim 1,wherein the sensor-specific determined likelihood represents determinedlikelihoods associated with the first sensor and determined likelihoodsassociated with the second sensor.
 9. The controller of claim 1, whereinthe regression model is a multinomial logistic regression model with agroup LASSO penalty.
 10. The controller of claim 9, wherein themultinomial logistic regression model is a binomial logistic regressionmodel with a group LASSO penalty.
 11. The controller of claim 1, whereinthe regression model determines weights of the grouped calculateddistances such that they best represent the first activity and thesecond activity.
 12. The controller of claim 1, wherein the processexecuted by the controller further comprises: identifying, from the atleast two sensors, a minimal set of sensors most salient for sensing thefirst activity and the second activity.
 13. The controller of claim 12,wherein the minimal set of sensors is selected as a subset of the atleast two sensors.
 14. The controller of claim 1, wherein the firstmodel, the second model, the third model and the fourth model eachcomprise a separate hidden Markov model.
 15. The controller of claim 1,wherein the arrangement is defined by characteristics of the space beingmonitored.
 16. A method for determining an arrangement of sensors formonitoring a space, comprising: receiving, from a first sensor of atleast two sensors, a first sensor data comprising at least onetime-series observation representing at least a first activity and asecond activity; receiving, from a second sensor of the at least twosensors, a second sensor data comprising at least one time-seriesobservation representing the first activity and the second activity;generating, by a processor, a first model for the first activityinvolving a first progression through a plurality of states indicated byat least a portion of the first sensor data; generating, by theprocessor, a second model for the second activity involving a secondprogression through a plurality of states indicated by at least aportion of the first sensor data; generating, by the processor, a thirdmodel for the first activity involving a third progression through aplurality of states indicated by at least a portion of the second sensordata; generating, by the processor, a fourth model for the secondactivity involving a fourth progression through a plurality of statesindicated by at least a portion of the second sensor data; receiving,from the first sensor, a third sensor data comprising at least onetime-series observation representing at least the first activity and thesecond activity; receiving, from the second sensor, a fourth sensor datacomprising at least one time-series observation representing at leastthe first activity and the second activity; determining, using theprocessor, a likelihood that the first model generated at least aportion of the third sensor data, a likelihood that the second modelgenerated at least a portion of the third sensor data, a likelihood thatthe third model generated at least a portion of the fourth sensor data,and a likelihood that the fourth model generated at least a portion ofthe fourth sensor data; calculating, using the processor, a pair-wisedistance between each sensor-specific determined likelihood to obtaincalculated distances; grouping, using the processor, the calculateddistances for the likelihoods involving the first sensor, and grouping,using the processor, the calculated distances for the likelihoodsinvolving the second sensor, to obtain grouped calculated distances; anddetermining, using the processor, a first relevance of the first sensorand a second relevance of the second sensor for capturing the firstactivity and the second activity by executing a regression model usingthe grouped calculated distances, wherein one of the first sensor andthe second sensor is included in an arrangement of sensors used tomonitor the space based on the first relevance and the second relevancedetermined using the processor, and the other of the first sensor andthe second sensor is excluded in the arrangement of sensors used tomonitor the space based on the first relevance and the second relevancedetermined using the processor.
 17. The method of claim 16, furthercomprising: arranging a minimized group of sensors to monitor the spacebased on the at least two sensors, wherein the minimized group excludesat least one of the at least two sensors.
 18. A system for determiningan arrangement of sensors for monitoring a space, comprising: acommunication interface used to communicate over a communicationsnetwork; a user interface; and a controller comprising a memory thatstores instructions, and a processor that executes the instructions,wherein, when executed by the processor, the instructions cause thesystem to execute a process comprising: receiving, from a first sensorof at least two sensors, a first sensor data comprising at least onetime-series observation representing at least a first activity and asecond activity; receiving, from a second sensor of the at least twosensors, a second sensor data comprising at least one time-seriesobservation representing the first activity and the second activity;generating, by the processor, a first model for the first activityinvolving a first progression through a plurality of states indicated byat least a portion of the first sensor data; generating, by theprocessor, a second model for the second activity involving a secondprogression through a plurality of states indicated by at least aportion of the first sensor data; generating, by the processor, a thirdmodel for the first activity involving a third progression through aplurality of states indicated by at least a portion of the second sensordata; generating, by the processor, a fourth model for the secondactivity involving a fourth progression through a plurality of statesindicated by at least a portion of the second sensor data; receiving,from the first sensor, a third sensor data comprising at least onetime-series observation representing at least the first activity and thesecond activity; receiving, from the second sensor, a fourth sensor datacomprising at least one time-series observation representing at leastthe first activity and the second activity; determining, using theprocessor, a likelihood that the first model generated at least aportion of the third sensor data, a likelihood that the second modelgenerated at least a portion of the third sensor data, a likelihood thatthe third model generated at least a portion of the fourth sensor data,and a likelihood that the fourth model generated at least a portion ofthe fourth sensor data; calculating, using the processor, a pair-wisedistance between each sensor-specific determined likelihood to obtaincalculated distances; grouping, using the processor, the calculateddistances for the likelihoods involving the first sensor, and grouping,using the processor, the calculated distances for the likelihoodsinvolving the second sensor, to obtain grouped calculated distances; anddetermining, using the processor, a first relevance of the first sensorand a second relevance of the second sensor for capturing the firstactivity and the second activity by executing a regression model usingthe grouped calculated distances, wherein one of the first sensor andthe second sensor is included in an arrangement of sensors used tomonitor the space based on the first relevance and the second relevancedetermined using the processor, and the other of the first sensor andthe second sensor is excluded in the arrangement of sensors used tomonitor the space based on the first relevance and the second relevancedetermined using the processor.
 19. The system of claim 18, wherein thespace comprises an enclosed living space.